Use of Application programming interfaces and open data … 5
* Introduction … 5
* Definition of “open insurance” and use cases … 6
* Possible risks and challenges for developing open insurance … 7
* Adequacy of current framework … 8
* Barriers, incentives and compulsion … 9
* Sequencing of open insurance adoption … 9
* Conclusions and next steps … 9
Distributed ledger technologies and blockchain … 10
* Introduction … 10
* DLT in insurance … 10
* Potential benefits for the insurance industry and consumers … 11
* Risks for regulatory and supervisory consideration … 11
* Conclusion and next steps … 14
Artificial intelligence and machine learning … 14
* Introduction … 14
* Model Risk Management and Governance … 14
* Data usage and management … 16
* Ethics, Bias and Discrimination … 16
* Conclusions and next steps … 18
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Introduction
Given the rapidly increasing ease of accessibility and digital innovation in financial technology (“FinTech”) and its far-reaching effects on the insurance sector, the
* Use of Application programming interfaces (APIs) and open data;
* Distributed ledger technologies (DLTs) and blockchain; and
* Safe, fair and ethical adoption of Artificial intelligence (AI) and Machine learning (ML) and the use and governance of data.
Assessment activities included input gathered through member surveys and interviews with market participants and experts. The purpose of the deep-dive assessments was to better understand the current digital transformation landscape, identify issues and trends in specific areas and assess their potential implications for insurance supervision.
This report presents the high-level findings of these assessments for information purposes. It is not intended to present a final assessment on the risks and opportunities of these trends; it also does not aim to state a preference as the IAIS takes a technology neutral approach. The IAIS will continue to monitor these trends and their impact on insurers, consumers and supervisory objectives.
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Conclusions and next steps
The use of AI/ML and big data by insurers may lead to supervisory concerns. Across the IAIS membership, several supervisors have issued high-level guidance around various thematic areas, including that of the use of alternative data, model risk management, third-party vendors and/or fair use of data. The role of the IAIS will be to take stock of these initiatives and identify good practices as well as potential gaps. 4.5.1 Analysing existing guidance/providing new guidance
* How the current regulatory framework could/could not apply to AI/ML;
* Whether additional clarification may be helpful;
* How policy can best support and further safeguard AI/ML use by taking into account and leveraging on wider and general regulations such as data/information protection and general consumer ethics/conducts outside of insurance regulations; and
* Necessary guidelines or standards on AI/ML needed to explicitly supplement conventional policy frameworks and impact the sustainability of expected future insurance business models, if any.
More concrete, practical guidance on the use of AI/ML that could include not only principles, but also use cases as well as a discussion on how existing regulation is enforced, should be considered.
In addition, any guidance proposed by the IAIS should ideally suggest the type of skills and resources (especially technical ones) needed to put in place such systems, particularly with regards to AI audit and bias analysis capability. Specific areas of interest for possible further exploration include actuarial questions such as the boundary between risk differentiation and unfair discrimination, and more technical ones (eg which methodology is most adequate for validating AI-based insurance engines on pricing/underwriting/investments/claims/valuation reserving and solvency).
4.5.2 Monitoring developments in ethics and fairness
Despite the importance assigned to the topic of discriminatory biases by most members, concrete deliverables by the
4.5.3 Alternative data
The use of alternative data is indeed relevant both to model risk management and to IoT (a major source of such data) which are two key study themes identified for the
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The report is posted at: https://www.iaisweb.org/uploads/2022/12/IAIS-Report-on-FinTech-developments-in-the-insurance-sector.pdf
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